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Found 4 papers, 3 papers with code
Jet Discrimination with Quantum Complete Graph Neural Network
Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications.
Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory.
Deep Learning Jet Substructure from Two-Particle Correlation
Deciphering the complex information contained in jets produced in collider events requires a physical organization of the jet data.
High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.
